32 modelling-and-simulation-of-combustion-postdoc PhD positions at Chalmers University of Technology
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evaluation frameworks and/or the development of energy system optimization models. The research is applied and closely linked to industrial interests and needs. About the research Our research aims to provide
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This PhD position offers a unique opportunity to advance safe and transparent control for autonomous, over-actuated electric vehicles. You will work at the intersection of model predictive control
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division at the department of Electrical engineering at Chalmers. Here, a team of PhD students, post-docs and senior researchers are working on modeling and numerical optimization of problems in the areas
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found in the areas of: Human-Technology Interaction Form and Function Modeling and Simulation Product Development Material Production and in the interaction between these areas. The research covers
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Technology Laboratory (QTL) division of the Microtechnology and Nanoscience (MC2) department, working in a large team of PhDs, postdocs and researchers. About the research We are seeking PhD students to work
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the Wastewater Management and Environmental Biotechnology research group. You will be supervised by three senior researchers and work alongside other PhD students and postdocs in the group. Our interdisciplinary
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interfaces and driver modelling Implementation of control algorithms in mechatronic systems Experimental design and statistical methods Vehicle testing and test methods involving human test subjects What you
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postdocs at Chalmers, and collaborate with academic and industrial partners in Sweden and internationally. The role also offers opportunities for travel and engagement with external collaborators. Research
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of quick clays. A novel combination of miniaturised thermal-hydro-mechanical experiments and particle level modelling will be pursued to unravel the unique mechanisms that make quick clays so hazardous and
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on the hypothesis that the future of building design lies at the intersection of physically sound building simulation models and machine learning (ML) techniques. Key considerations include effectively integrating ML